Encyclopedia of Computational Neuroscience

2015 Edition
| Editors: Dieter Jaeger, Ranu Jung

Efficient Population Coding

Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6675-8_578

Synonyms

Definition

Natural stimulations caused by objects in the surrounding world do not stimulate single sensory receptors in isolation but lead to the activation of large numbers of neurons simultaneously. Thus, typical stimulus variables of interest are represented only implicitly in activation patterns across large neural populations. These patterns are statistical in nature since repeated presentation of the same stimulus usually leads to highly variable responses. The large dimensionality and randomness of the neural responses make it difficult to assess how well different stimuli can be discriminated. Depending on how effectively neurons share the labor of encoding, the accuracy with which stimuli are represented can change dramatically. Thus, studying the efficiency of population codes is important for our understanding of both which information is...

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References

  1. Abbott L, Dayan P (1999) The effect of correlated variability on the accuracy of a population code. Neural Comput 11(1):91–101PubMedGoogle Scholar
  2. Beck J, Ma W, Pitkow X, Latham P, Pouget A (2012) Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron 74(1):30–39PubMedGoogle Scholar
  3. Berens P, Gerwinn S, Ecker AS, Bethge M (2009) Neurometric function analysis of population codes. In: Advances in neural information processing systems, vol 22. Vancouver, BC, CanadaGoogle Scholar
  4. Berens P, Ecker AS, Gerwinn S, Tolias AS, Bethge M (2011) Reassessing optimal neural population codes with neurometric functions. Proc Natl Acad Sci USA 108(11):4423–4428PubMedCentralPubMedGoogle Scholar
  5. Bethge M, Rotermund D, Pawelzik K (2002) Optimal short-term population coding: when fisher information fails. Neural Comput 14(10):2317–2351PubMedGoogle Scholar
  6. Bethge M, Rotermund D, Pawelzik K (2003a) Optimal neural rate coding leads to bimodal firing rate distributions. Network Comput Neural Syst 14(2):303–319Google Scholar
  7. Bethge M, Rotermund D, Pawelzik K (2003b) Second order phase transition in neural rate coding: binary encoding is optimal for rapid signal transmission. Phys Rev Lett 90(8):088104PubMedGoogle Scholar
  8. Brunel N, Nadal J-P (1998) Mutual information, fisher information, and population coding. Neural Comput 10(7):1731–1757PubMedGoogle Scholar
  9. Dayan P, Abbott LF (2001) Theoretical neuroscience, vol 31. MIT Press, Cambridge, MAGoogle Scholar
  10. Ecker AS, Berens P, Tolias AS, Bethge M (2011) The effect of noise correlations in populations of diversely tuned neurons. J Neurosci 31(40):14272–14283PubMedCentralPubMedGoogle Scholar
  11. Haefner R, Bethge M (2010) Evaluating neuronal codes for inference using fisher information. In: Advances in neural information processing systems, vol 23. Vancouver, BC, CanadaGoogle Scholar
  12. Haefner RM, Gerwinn S, Macke JH, Bethge M (2013) Inferring decoding strategies from choice probabilities in the presence of correlated variability. Nat Neurosci 16:235–242PubMedGoogle Scholar
  13. Huys QJ, Zemel RS, Natarajan R, Dayan P (2007) Fast population coding. Neural Comput 19(2):404–441PubMedGoogle Scholar
  14. Klam F, Zemel RS, Pouget A (2008) Population coding with motion energy filters: the impact of correlations. Neural Comput 20(1):146–175PubMedGoogle Scholar
  15. Mathis A, Herz AVM, Stemmler MB (2012) Optimal population codes for space: grid cells outperform place cells. Neural Comput 24(9):2280–2317PubMedGoogle Scholar
  16. McDonnell MD, Stocks NG (2008) Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations. Phys Rev Lett 101(5):058103PubMedGoogle Scholar
  17. Nadal J-P, Parga N (1994) Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer. Network Comput Neural Syst 5(4):565–581Google Scholar
  18. Salinas E, Abbott L (1994) Vector reconstruction from firing rates. J Comput Neurosci 1(1–2):89–107PubMedGoogle Scholar
  19. Shamir M (2014) Emerging principles of population coding: in search for the neural code. Curr Opin Neurobiol 25:140–148PubMedGoogle Scholar
  20. Shamir M, Sompolinsky H (2004) Nonlinear population codes. Neural Comput 16(6):1105–1136PubMedGoogle Scholar
  21. Shamir M, Sompolinsky H (2006) Implications of neuronal diversity on population coding. Neural Comput 18(8):1951–1986PubMedGoogle Scholar
  22. Sreenivasan S, Fiete I (2011) Grid cells generate an analog error-correcting code for singularly precise neural computation. Nat Neurosci 14(10):1330–1337PubMedGoogle Scholar
  23. Yarrow S, Challis E, Series P (2012) Fisher and shannon information in finite neural populations. Neural Comput 24(7):1740–1780PubMedGoogle Scholar
  24. Zohary E, Shadlen MN, Newsome WT (1994) Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370(6485):140–143PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen and Max Planck Institute for Biological CyberneticsTübingenGermany